Papers by Moshe Wasserblat
Optimizing Retrieval-augmented Reader Models via Token Elimination (2023.emnlp-main)
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| Challenge: | Existing methods for ODQA use a retrieval-augmented language model . a generative model can cause a significant bottleneck in decoding time . |
| Approach: | They propose to eliminate some of the retrieved information that might not contribute essential information to the answer generation process. |
| Outcome: | The proposed method reduces run-time by up to 62.2% with only 2% reduction in performance and improves performance. |
ABSApp: A Portable Weakly-Supervised Aspect-Based Sentiment Extraction System (D19-3)
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| Challenge: | a portable system for weakly-supervised aspect-based sentiment extraction is presented . ABSApp is a weakly supervised aspect based sentiment analysis system . |
| Approach: | They present a portable system for weakly-supervised aspect-based sentiment extraction . ABSApp generates domain-specific aspect and opinion lexicons based on unlabeled dataset . |
| Outcome: | The proposed system is interpretable and user friendly and can be quickly and cost-effectively used across domains . it generates domain-specific aspect and opinion lexicons, edits them, and generates an aspect-based sentiment report . the system has been successfully used in movie review analysis and convention impact analysis . |
CoTAR: Chain-of-Thought Attribution Reasoning with Multi-level Granularity (2024.findings-emnlp)
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| Challenge: | State-of-the-art QA systems employ Large Language Models (LLMs) however, these models tend to hallucinate information in their responses. |
| Approach: | They propose an attribution-oriented Chain-of-Thought reasoning method to enhance attributions. |
| Outcome: | The proposed method outperforms existing models on context enhanced question-answering datasets and shows that it can be used to improve accuracy. |
Term Set Expansion based NLP Architect by Intel AI Lab (D18-2)
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Jonathan Mamou, Oren Pereg, Moshe Wasserblat, Alon Eirew, Yael Green, Shira Guskin, Peter Izsak, Daniel Korat
| Challenge: | SetExpander is a corpus-based system for expanding a seed set of terms into a more complete set of words belonging to the same semantic class. |
| Approach: | They propose a corpus-based system for expanding a seed set of terms into a more complete set of words that belong to the same semantic class. |
| Outcome: | The proposed system can expand a seed set of terms into a more complete set of words belonging to the same semantic class. |
InterpreT: An Interactive Visualization Tool for Interpreting Transformers (2021.eacl-demos)
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Vasudev Lal, Arden Ma, Estelle Aflalo, Phillip Howard, Ana Simoes, Daniel Korat, Oren Pereg, Gadi Singer, Moshe Wasserblat
| Challenge: | Using Transformer-based models for NLU/NLP tasks is a growing interest . but there are many open questions regarding the behavior of these models . |
| Approach: | They present an interactive visualization tool for interpreting Transformer-based models. |
| Outcome: | The tool can track and visualize token embeddings through each layer of a Transformer, highlight distances between certain token embeds, and identify task-related functions of attention heads using new metrics. |
SetExpander: End-to-end Term Set Expansion Based on Multi-Context Term Embeddings (C18-2)
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Jonathan Mamou, Oren Pereg, Moshe Wasserblat, Ido Dagan, Yoav Goldberg, Alon Eirew, Yael Green, Shira Guskin, Peter Izsak, Daniel Korat
| Challenge: | SetExpander is a corpus-based system for expanding a seed set of terms into a more complete set of words belonging to the same semantic class. |
| Approach: | They propose to use a corpus-based system for expanding a seed set of terms into a more complete set of words that belong to the same semantic class. |
| Outcome: | The proposed system can expand a seed set of terms, validate it, re-expand the expanded set and store it, thus simplifying the extraction of domain-specific fine-grained semantic classes. |
Syntactically Aware Cross-Domain Aspect and Opinion Terms Extraction (2020.coling-main)
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| Challenge: | Supervised-learning approaches fail to scale across domains where labeled data is lacking. |
| Approach: | They propose a method for incorporating external linguistic knowledge into a self-attention mechanism coupled with a transformer-based model. |
| Outcome: | The proposed method enables leveraging syntactic knowledge from transformer-based models to bridge the gap between domains. |